Mastercard
Budapest, Hungary
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.
Senior ML Platform Engineer
Our Mission
At Mastercard Identity Verification, we build data, machine learning, and platform capabilities that help customers make safer decisions in digital commerce. Our technologies enable merchants and partners to assess transaction risk, detect fraud, and establish trust using large-scale identity and behavioral signals.
Within the IDV Data Science organization, the Machine Learning Platform (MLP) team develops internal data platforms, engineering foundations, and tooling that support machine learning solutions across our products. We work at the intersection of software engineering, data engineering, distributed computing, and machine learning, transforming large-scale data into production systems that help protect billions of digital interactions every year.
Overview
We are looking for a Senior ML Platform Engineer to join our Budapest team.
This is a hands-on engineering role focused on building, developing, and maintaining internal platforms, tools, and distributed data processing capabilities that enable reliable, production-grade machine learning workflows. The role is centered on software, data, and ML engineering, with platform work meaning the internal capabilities that help Data Scientists and Engineers build, run, and improve machine learning systems at scale.
The ideal candidate is a strong software engineer who enjoys solving complex data and machine learning engineering challenges, writing high-quality code, and building systems that operate reliably at scale.
In This Role You Will
Build and evolve internal data and machine learning platforms used across IDV Data Science.
Develop distributed data processing solutions, reusable tooling, and platform capabilities for internal machine learning workflows at scale.
Improve reliability, performance, automation, and maintainability across the engineering systems and tools we build.
Collaborate with Data Scientists and engineers to bring machine learning solutions into production.
Contribute to technical design, architecture, and engineering best practices.
Mentor teammates and help raise the engineering bar across the organization.
All About You
Strong software engineering background with professional experience in JVM languages; Scala experience is preferred, but strong Java or Kotlin experience is also welcome where paired with a willingness to learn.
Solid Python development experience.
Deep understanding of distributed data processing systems, preferably Apache Spark.
Experience designing and operating large-scale data pipelines, platforms, or machine learning systems.
Knowledge of MLOps, Data Engineering, and data warehousing concepts.
Strong problem-solving skills, a pragmatic engineering mindset, and natural curiosity for learning new technologies and approaches.
Excellent communication and collaboration skills.
Nice to have:
Experience with, or interest in, functional programming in a strongly typed setting.
Databricks experience.
AWS or other cloud platform experience.
Experience with modern AI-assisted development tools such as GitHub Copilot, Claude, or similar.
Why Join Us
Work on large-scale systems that help customers combat fraud and build trust in digital commerce.
Influence the architecture and direction of internal machine learning platforms and tools used by Data Science teams.
Collaborate with experienced Data Scientists, Engineers, and Architects.
Tackle challenging problems involving distributed systems, data, and machine learning at scale.
Be part of a team that values technical excellence, ownership, curiosity, learning, and continuous improvement.
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: